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Fernando, Joe

Publications and source records attributed to Fernando, Joe.

18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lidar - ND Halo Scanning Doppler, Boardman - Processed Data

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to the attached "WFIP2 Project (lidar.z07)" Readme file.

17 WIND ENERGY↗
DOE OSTI2023

Lidar - ND Halo Scanning Doppler, Boardman - Derived Data

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to the attached "WFIP2 Project (lidar.z07)" Readme file.

17 WIND ENERGY↗
DOE OSTI2021

Lidar - ND Halo Scanning Doppler, Boardman - Raw Data

The University of Notre Dame (ND) scanning lidar dataset used for the WFIP2 Campaign is provided. The raw dataset contains the radial velocity and backscatter measurements along with the beam location and other lidar parameters in the header.

17 WIND ENERGY↗
DOE OSTI2021

Lidar - ND Halo Scanning Doppler, Boardman - Reviewed Data

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to Section 4.c) in the updated version of the "WFIP2 Project (lidar.z07)" Readme file, where the lidar.z07.b0 dataset is fully explained.

17 WIND ENERGY↗
DOE OSTI2019

Wind Forecasting Improvement Project In Complex Terrain Near the Columbia River Gorge

In 2014 a multi-institution team led by Vaisala, Inc. was selected by the Department of Energy (DOE) to partner with multiple DOE and National Oceanic and Atmospheric Administration (NOAA) laboratories on a project designed to improve the quality of wind power forecasts in areas of complex terrain. This was the second Wind Forecast Improvement Project (WFIP2) funded by DOE and it extended from late 2014 through the middle of 2018. It encompassed an 18-month observational field campaign, numerical weather prediction (NWP) model development, extensive analysis of data and NWP output, and the creation of decision support tool algorithms to convey forecast information to end users in the wind industry. WFIP2 focused on improvements to the representation of near-surface and boundary-layer physics in NOAA’s High-Resolution Rapid Refresh (HRRR) model. Improvements to HRRR, which is run operationally over the continental United States, benefit the wind industry in multiple ways. Forecasts from the operational HRRR are used directly by wind power forecast vendors and the operators of wind plants. In addition, because HRRR is built using the widely-used Weather Research and Forecasting (WRF) model, improvements to its parameterizations become available to commercial and research institutions using WRF for a myriad of purposes. The geographic area studied by WFIP2 was a region of the Columbia River basin located to the east of the Cascade Mountains between Oregon and Washington. Home to over 6 GW of installed capacity for wind energy production, this area also hosts a variety of atmospheric phenomena either unique to or augmented by complex topography. This makes it an attractive test-bed for the analysis of wind forecast in complex terrain, though results found are should be applicable in any area of topographic complexity. WFIP2 succeeded as a collaborative effort, and while this report focuses on the activities of the team led by Vaisala, the work described here is part of a larger whole. The Vaisala team accomplished a number of specific tasks as described in this report, while also contributing to this larger effort.

17 WIND ENERGY↗